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🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We...

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Bo Wang

30,488 subscribers

178,451 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 10

Фото профиля SkalskiP
SkalskiP1 год назад

So cool to see a new wave of papers using SAM2!

Фото профиля parm
parm1 год назад

I cannot get over the left video (bottom left)

Фото профиля Tanishq Mathew Abraham, Ph.D.
Tanishq Mathew Abraham, Ph.D.1 год назад

great work, congrats to all the authors involved...

Фото профиля parm
parm1 год назад

WOAH!

Фото профиля parm
parm1 год назад

@iScienceLuvr !!! Time to nerd out on this

Фото профиля Degui Zhi
Degui Zhi1 год назад

Wow! You are so fast!

Фото профиля Faizan Cheema
Faizan Cheema1 год назад

this is beautiful 😊 makes me excited for the future

Фото профиля Krish Dasgupta
Krish Dasgupta1 год назад

That’s so quick ! Amazing. @ryancarson check this out. Was telling about this the other day.

Фото профиля Shakuntala Baichoo
Shakuntala Baichoo1 год назад

Proud to be part of this incredible lab pushing the boundaries of AI and health! Amazing work and inspiring team!

Фото профиля Hamza Mahdi
Hamza Mahdi1 год назад

Excited to try it out!

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Super excited to introduce SAM2 Studio! 🚀🤖 I've been getting a lot of questions lately on supporting AI inference tailored for patient data and sensitive workflows. We optimized SAM2 to run completely on-device in real time for all of your medical segmentation workflows - including surgical video segmentation, radiographs, pathology slides and more!
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Super excited to introduce SAM2 Studio! 🚀🤖 I've been getting a lot of questions lately on supporting AI inference tailored for patient data and sensitive workflows. We optimized SAM2 to run completely on-device in real time for all of your medical segmentation workflows - including surgical video segmentation, radiographs, pathology slides and more!

Cyril Zakka, MD

19,805 просмотров • 1 год назад

🎉 The best way to start the week is to find out that our MedSAM is finally published today in Nature Communications! **Segment anything in medical images** Paper: arXiv: Data & Code: MedSAM is the first promotable foundation model for medical image segmentation. **Highlights**: ⭐ Before its formal publication, we have received 220 citations and 1400+ GitHub stars 🙏🙏❤️‍🔥❤️‍🔥❤️‍🔥 📊 We curated a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. 🚀 Built on top of SAM (AI at Meta ) with transfer learning, we have significantly enhanced its segmentation performance of medical images. 📈 Comprehensive evaluations of 86 internal validation tasks and 60 external validation tasks demonstrate its better accuracy and robustness than modality-wise specialist models. **What is Next? --- Clinical Translation!!** 🍕Our next goal is to make the model deployable on laptops (CPUs) or other edge devices without reliance on GPUs. We have distilled a lightweight model, LiteMedSAM, offering a speed boost of 10x while maintaining accuracy. Plus, we have integrated it into the 3D Slicer plugin, providing an efficient tool for medical image segmentation. 🌐 To further promote developments in this field, we organize a competition on #CVPR2026: Segment Anything in Medical Images on Laptop! An out-of-the-box baseline has been released to reduce the entry barriers. Welcome to join us to push the boundary further: 🙏 Massive thanks to MetaAI AI at Meta for their open-source project SAM and many reviewers/users for their invaluable feedback. A huge shoutout to my postdoc Jun Ma (JunMa) for his leadership on this project!! UHN AI Hub Vector Institute Peter Munk Cardiac Centre AI Department of Laboratory Medicine & Pathobiology U of T Department of Computer Science University of Toronto University Health Network Brad Wouters 🇨🇦 Barry Rubin MD, PhD, FRCSC Shaf Keshavjee
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🎉 The best way to start the week is to find out that our MedSAM is finally published today in Nature Communications! **Segment anything in medical images** Paper: arXiv: Data & Code: MedSAM is the first promotable foundation model for medical image segmentation. **Highlights**: ⭐ Before its formal publication, we have received 220 citations and 1400+ GitHub stars 🙏🙏❤️‍🔥❤️‍🔥❤️‍🔥 📊 We curated a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. 🚀 Built on top of SAM (AI at Meta ) with transfer learning, we have significantly enhanced its segmentation performance of medical images. 📈 Comprehensive evaluations of 86 internal validation tasks and 60 external validation tasks demonstrate its better accuracy and robustness than modality-wise specialist models. **What is Next? --- Clinical Translation!!** 🍕Our next goal is to make the model deployable on laptops (CPUs) or other edge devices without reliance on GPUs. We have distilled a lightweight model, LiteMedSAM, offering a speed boost of 10x while maintaining accuracy. Plus, we have integrated it into the 3D Slicer plugin, providing an efficient tool for medical image segmentation. 🌐 To further promote developments in this field, we organize a competition on #CVPR2026: Segment Anything in Medical Images on Laptop! An out-of-the-box baseline has been released to reduce the entry barriers. Welcome to join us to push the boundary further: 🙏 Massive thanks to MetaAI AI at Meta for their open-source project SAM and many reviewers/users for their invaluable feedback. A huge shoutout to my postdoc Jun Ma (JunMa) for his leadership on this project!! UHN AI Hub Vector Institute Peter Munk Cardiac Centre AI Department of Laboratory Medicine & Pathobiology U of T Department of Computer Science University of Toronto University Health Network Brad Wouters 🇨🇦 Barry Rubin MD, PhD, FRCSC Shaf Keshavjee

Bo Wang

140,180 просмотров • 2 лет назад

Exciting news! Our MedSAM is featured in Computer Vision News! 🎉🎉🎉🙌 Check it out: We are not stopping at papers and aiming to take integration to the next level by incorporating MedSAM with 3D Slicer, a top-tier medical imaging viewer. 🏥 **Segment Anything in Medical Images!** Read the paper here: Get your hands on the code: Explore MedSAM@3Dslice: Huge shoutout to the lead contributor, Jun Ma (JunMa), and a big thank you to Reza Asakereh for his invaluable contributions to MedSAMSlicer! 👏 #MedSAM #MedicalImaging
1:00

Exciting news! Our MedSAM is featured in Computer Vision News! 🎉🎉🎉🙌 Check it out: We are not stopping at papers and aiming to take integration to the next level by incorporating MedSAM with 3D Slicer, a top-tier medical imaging viewer. 🏥 **Segment Anything in Medical Images!** Read the paper here: Get your hands on the code: Explore MedSAM@3Dslice: Huge shoutout to the lead contributor, Jun Ma (JunMa), and a big thank you to Reza Asakereh for his invaluable contributions to MedSAMSlicer! 👏 #MedSAM #MedicalImaging

Bo Wang

19,527 просмотров • 2 лет назад

Tracking Anything with Decoupled Video Segmentation paper page: Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task (which is cheaper to train) and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we use bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several data-scarce tasks including large-vocabulary video panoptic segmentation, open-world video segmentation, referring video segmentation, and unsupervised video object segmentation.
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Tracking Anything with Decoupled Video Segmentation paper page: Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task (which is cheaper to train) and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we use bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several data-scarce tasks including large-vocabulary video panoptic segmentation, open-world video segmentation, referring video segmentation, and unsupervised video object segmentation.

AK

305,560 просмотров • 2 лет назад

The Segment Anything Model (SAM) by Meta AI is a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from photos or videos + zero-shot transfer to other segmentation tasks. Try the demo ➡️
0:51

The Segment Anything Model (SAM) by Meta AI is a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from photos or videos + zero-shot transfer to other segmentation tasks. Try the demo ➡️

AI at Meta

186,324 просмотров • 3 лет назад

✨ CVPR 2025 highlight: A Distractor-Aware Memory for Visual Object Tracking with SAM2 the authors propose a new distractor-aware memory model for SAM2 and an introspection-based update strategy that jointly addresses the segmentation accuracy as well as tracking robustness 🏡 (1/n)🧵👇
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✨ CVPR 2025 highlight: A Distractor-Aware Memory for Visual Object Tracking with SAM2 the authors propose a new distractor-aware memory model for SAM2 and an introspection-based update strategy that jointly addresses the segmentation accuracy as well as tracking robustness 🏡 (1/n)🧵👇

GeekyRakshit (e/mad)

32,669 просмотров • 1 год назад

Hitem3D V2.0 just launched. It's an AI 3D model generation tool that does three things: - Photo or image to full 3D model - Model segmentation so you can isolate and refine parts - Image to STL for 3D printing Combine this with Google Nano Banana and you can build game assets, product prototypes, and printable designs in minutes. Here's what I tested. Sponsored by Hi3D 1/4
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Hitem3D V2.0 just launched. It's an AI 3D model generation tool that does three things: - Photo or image to full 3D model - Model segmentation so you can isolate and refine parts - Image to STL for 3D printing Combine this with Google Nano Banana and you can build game assets, product prototypes, and printable designs in minutes. Here's what I tested. Sponsored by Hi3D 1/4

AshutoshShrivastava

19,574 просмотров • 3 месяцев назад

Apache 2.0 license🔥 On-device deployment ready Extends SAM2 for tracking objects in videos 🔥 Click-to-segment support EdgeTAM by Meta !
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Apache 2.0 license🔥 On-device deployment ready Extends SAM2 for tracking objects in videos 🔥 Click-to-segment support EdgeTAM by Meta !

Gradio

23,301 просмотров • 1 год назад

zero-shot image segmentation with Florence-2 + SAM-2 combo just added new mode to my Hugging Face; now you can run open vocabulary detection with Florence-2 + box to mask with SAM2 link:
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zero-shot image segmentation with Florence-2 + SAM-2 combo just added new mode to my Hugging Face; now you can run open vocabulary detection with Florence-2 + box to mask with SAM2 link:

SkalskiP

65,883 просмотров • 1 год назад

SAM 2 from Meta FAIR is the first unified model for real-time, promptable object segmentation in images & videos. Using the model in our web-based demo you can segment, track and apply effects to objects in video in just a few clicks. Try SAM 2 ➡️
0:40

SAM 2 from Meta FAIR is the first unified model for real-time, promptable object segmentation in images & videos. Using the model in our web-based demo you can segment, track and apply effects to objects in video in just a few clicks. Try SAM 2 ➡️

AI at Meta

88,918 просмотров • 1 год назад

SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians Contributions: • We propose SuperGSeg: a 3D segmentation method with neural Gaussians, designed to learn hierarchical instance segmentation features from 2D foundation models. • We introduce the concept of Super-Gaussian, a novel representation that integrates hierarchical instance segmentation features, enabling the embedding of high-dimensional language features. This approach addresses previously unfeasible challenges in representing complex scenes with rich semantic details. • Extensive experiments on the LERF-OVS and ScanNet datasets demonstrate the effectiveness of the proposed method, achieving significant improvements in open-vocabulary 3D object-level and scene-level semantic segmentation. It shows particular strength in capturing fine-grained scene details and dense pixel semantic segmentation tasks for the first time.
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SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians Contributions: • We propose SuperGSeg: a 3D segmentation method with neural Gaussians, designed to learn hierarchical instance segmentation features from 2D foundation models. • We introduce the concept of Super-Gaussian, a novel representation that integrates hierarchical instance segmentation features, enabling the embedding of high-dimensional language features. This approach addresses previously unfeasible challenges in representing complex scenes with rich semantic details. • Extensive experiments on the LERF-OVS and ScanNet datasets demonstrate the effectiveness of the proposed method, achieving significant improvements in open-vocabulary 3D object-level and scene-level semantic segmentation. It shows particular strength in capturing fine-grained scene details and dense pixel semantic segmentation tasks for the first time.

MrNeRF

13,594 просмотров • 1 год назад

Meet SAM 3, a unified model that enables detection, segmentation, and tracking of objects across images and videos. SAM 3 introduces some of our most highly requested features like text and exemplar prompts to segment all objects of a target category. Learnings from SAM 3 will help power new features in Instagram Edits and Vibes, bringing advanced segmentation capabilities directly to creators. 🔗 Learn more:
1:09

Meet SAM 3, a unified model that enables detection, segmentation, and tracking of objects across images and videos. SAM 3 introduces some of our most highly requested features like text and exemplar prompts to segment all objects of a target category. Learnings from SAM 3 will help power new features in Instagram Edits and Vibes, bringing advanced segmentation capabilities directly to creators. 🔗 Learn more:

AI at Meta

189,457 просмотров • 6 месяцев назад

Google has launched Med-Gemini, an advanced AI fine-tuned for medical Tasks. It significantly outperforms earlier models, including GPT-4, on most medical benchmarks. Achieves top scores, particularly on the MedQA-USMLE benchmark with a groundbreaking 91.1% accuracy.🚀 Demonstrates superior performance over GPT-4 by 44.5% on average across seven multimodal benchmarks. Excels in tasks such as medical summarization, generating doctor referrals, and simplifying medical documents. It is a preferred method over human expert analyses for complex text-based medical tasks. This marks a significant advancement in AI for healthcare, suggesting potential improvements in medical diagnostics and patient care. By choosing to harmonize with technology, we become more human, we become IRREPLACEABLE. Join the IRREPLACEABLE Academy and read the Book: #techforgood #medicine #ai
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Google has launched Med-Gemini, an advanced AI fine-tuned for medical Tasks. It significantly outperforms earlier models, including GPT-4, on most medical benchmarks. Achieves top scores, particularly on the MedQA-USMLE benchmark with a groundbreaking 91.1% accuracy.🚀 Demonstrates superior performance over GPT-4 by 44.5% on average across seven multimodal benchmarks. Excels in tasks such as medical summarization, generating doctor referrals, and simplifying medical documents. It is a preferred method over human expert analyses for complex text-based medical tasks. This marks a significant advancement in AI for healthcare, suggesting potential improvements in medical diagnostics and patient care. By choosing to harmonize with technology, we become more human, we become IRREPLACEABLE. Join the IRREPLACEABLE Academy and read the Book: #techforgood #medicine #ai

Pascal Bornet

14,393 просмотров • 1 год назад

Here’s a sneak peek using Rerun and Gradio for data annotation. It uses Video Depth Anything and Segment Anything 2 under the hood to generate segmentation masks and depth maps/point clouds. More to share next week.
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Here’s a sneak peek using Rerun and Gradio for data annotation. It uses Video Depth Anything and Segment Anything 2 under the hood to generate segmentation masks and depth maps/point clouds. More to share next week.

Pablo Vela

36,719 просмотров • 1 год назад

🚀 I am very excited to release the SamGeo QGIS plugin for geospatial image segmentation, powered by Meta’s Segment Anything Model (SAM 3) In this full tutorial, I’ll walk you through how to install, configure, and start segmenting satellite imagery in QGIS without writing a single line of code! 👉 Download the plugin here: 💻 Full video tutorial: #QGIS #SegmentAnything #SAM #GeoAI #RemoteSensing
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🚀 I am very excited to release the SamGeo QGIS plugin for geospatial image segmentation, powered by Meta’s Segment Anything Model (SAM 3) In this full tutorial, I’ll walk you through how to install, configure, and start segmenting satellite imagery in QGIS without writing a single line of code! 👉 Download the plugin here: 💻 Full video tutorial: #QGIS #SegmentAnything #SAM #GeoAI #RemoteSensing

Qiusheng Wu

28,928 просмотров • 6 месяцев назад

Segment Anything Model 2 (SAM 2) is a foundation model from Meta FAIR for promptable visual segmentation in images & videos. Available now for anyone to build on for free, open source under an Apache license. Try the demo ➡️
0:35

Segment Anything Model 2 (SAM 2) is a foundation model from Meta FAIR for promptable visual segmentation in images & videos. Available now for anyone to build on for free, open source under an Apache license. Try the demo ➡️

AI at Meta

97,730 просмотров • 1 год назад

Today we’re excited to unveil a new generation of Segment Anything Models: 1️⃣ SAM 3 enables detecting, segmenting and tracking of objects across images and videos, now with short text phrases and exemplar prompts. 🔗 Learn more about SAM 3: 2️⃣ SAM 3D brings the model collection into the 3rd dimension to enable precise reconstruction of 3D objects and people from a single 2D image. 🔗 Learn more about SAM 3D: These models offer innovative capabilities and unique tools for developers and researchers to create, experiment and uplevel media workflows.
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Today we’re excited to unveil a new generation of Segment Anything Models: 1️⃣ SAM 3 enables detecting, segmenting and tracking of objects across images and videos, now with short text phrases and exemplar prompts. 🔗 Learn more about SAM 3: 2️⃣ SAM 3D brings the model collection into the 3rd dimension to enable precise reconstruction of 3D objects and people from a single 2D image. 🔗 Learn more about SAM 3D: These models offer innovative capabilities and unique tools for developers and researchers to create, experiment and uplevel media workflows.

AI at Meta

1,087,064 просмотров • 6 месяцев назад

🚀 The GeoAI QGIS Plugin is here 🔥 You can run Moondream vision-language models, object detection, image segmentation (SAM 3), and even train your own geospatial segmentation model end-to-end. Website: GitHub: Short demo: Full video tutorial: #QGIS #GeoAI #SAM3 #Geospatial #DeepLearning #ComputerVision #OpenSource #Python
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🚀 The GeoAI QGIS Plugin is here 🔥 You can run Moondream vision-language models, object detection, image segmentation (SAM 3), and even train your own geospatial segmentation model end-to-end. Website: GitHub: Short demo: Full video tutorial: #QGIS #GeoAI #SAM3 #Geospatial #DeepLearning #ComputerVision #OpenSource #Python

Qiusheng Wu

11,682 просмотров • 6 месяцев назад

Meet Atilla K., SWE in Health AI at Google, whose work on CT Foundation is streamlining the development of AI models for 3D medical computed tomographic images. Learn more about how CT Foundation is making medical AI research more accessible & efficient:
1:58

Meet Atilla K., SWE in Health AI at Google, whose work on CT Foundation is streamlining the development of AI models for 3D medical computed tomographic images. Learn more about how CT Foundation is making medical AI research more accessible & efficient:

Google AI

32,244 просмотров • 1 год назад

🤯 OneDiffusion: A versatile, large-scale diffusion model that seamlessly supports bidirectional image synthesis and understanding across diverse tasks. ✅ Text to Image ✅ Image to Depth ✅ Image to Segmentation ✅ Image to Pose ✅ FaceID ✅ Image to Multiview How to use & more👇
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🤯 OneDiffusion: A versatile, large-scale diffusion model that seamlessly supports bidirectional image synthesis and understanding across diverse tasks. ✅ Text to Image ✅ Image to Depth ✅ Image to Segmentation ✅ Image to Pose ✅ FaceID ✅ Image to Multiview How to use & more👇

Gradio

11,820 просмотров • 1 год назад